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Research needed for focusing on additional generality of applied behavior analysis.

Most behavior analysts would agree that best-practice behavior-analytic services require, at a minimum, problem identification, establishing operational definitions, establishing assessment and treatments goals, achieving accurate data collection, and evaluating treatment in a reasonably conservative experimental design. Most behavior analysts would also agree that taking those steps in the context of problem solving should occur in a single-subject design. That is, behavior analysts are likely to focus on behavior change at the level of the individual response class rather than measures of central tendency within a large group of individuals (Johnston & Pennypacker, 1980).

Interestingly, this methodology (i.e., single-subject design and the focus in the individual) is relatively rare in psychological research (Friman, 2010). Establishing functional relations typically relies on determining the likelihood that one may draw an inference about the relationship between an independent variable and dependent variable at some arbitrarily agreed upon acceptable level of error (e.g., p = .05). This is in stark contrast to behavior analytic research and practice, in which statistics are relatively rare. The criterion for drawing an inference about the relationship between an independent variable and dependent variable is usually the judgment of a "visual inspector", who must decide if a reasonable demonstration of experimental control and change of social significance are evident when the data are depicted in graphical form (Baer, Wolf, & Risley, 1968). The advantages and disadvantages of these disparate approaches have been described, discussed, and argued elsewhere (e.g., Baer, 1977; Johnston & Pennypacker, 1980; Michael, 1974). The field of ABA, though, has almost categorically adopted single-subject methodology and logic as the core of its practice and research.

The maintenance and expansion of this adoption is not surprising, especially when the loci of service initiation are considered. Although behavior analysis includes active research agendas across many areas in psychology, contingencies have favored heavy growth of the field in the area of developmental disabilities in educational settings in particular. For example, behavior analysts in academia, private practice, group homes, and schools are very likely to be referred a school-aged (or younger) child or adolescent who engages in some form of "unacceptable" behavior. Present contingencies support this mode of service delivery. The Individuals with Disabilities Education Improvement Act (IDEIA; 2004) mandates functional behavior assessment (FBA) for the individual, depending on the circumstances. For instance, children diagnosed with autism often receive behavior analytic services, either through the school or outside of school, to ameliorate the "symptoms" of autism or to increase communication and social interaction. School systems often hire "behavior specialists" (who may or may not technically be trained in ABA) to respond to teachers' concerns complaints about individual students who require intervention. In sum, the target for assessment and intervention that is mandated and funded is typically the behavior of individual students, which aligns well with the philosophy and practice of most behavior analysts, maintaining and increasing the need for behavior analysts and their services.

The good news is that children who are the most critically and severely in need of services are more likely than ever to obtain evidence-based, scientifically reliable and valid, and effective assessment and treatment. The bad news is that there has been, arguably, an unhealthy emphasis in ABA to providing a large portion of its collective effort to a very small proportion of people who have problems of social significance to be solved (Friman, 2010; Woods, Miltenberger, & Carr, 2006). For example, one of the most well known "procedures" in ABA is the functional analysis technology developed by Iwata, Dorsey, Slifer, Bauman, and Richman (1982). The generality of this technology has been very well established across populations (Kern, Child, Dunlap, Clarke, & Falk, 1994; Kern, Mauk, Merder, & Mace, 1995; Lalli, Mace, Wohn, & Livezy, 1995), topographies (Borrero & Vollmer, 2006; Mace & Lalli, 1991; Piazza et al., 2003; Piazza et al., 1998), and therapists (Iwata et al., 2000; Moore & Fisher, 2007). Despite its generality in these areas, Hanley, Iwata, and McCord (2003) reported that 91.3% of published functional analyses (meeting particular criteria; see Hanley et al.) were conducted with individuals diagnosed with developmental disabilities. While this finding is good news for individuals diagnosed with developmental disabilities, questions remain about an important type of generality. Specifically, if one is interested in establishing a function-based treatment for in individual not diagnosed with developmental disabilities, is functional analysis the appropriate tool? What modifications, if any, may be necessary for conducting functional analyses with individuals not diagnosed with developmental disabilities? How might a similarly effective assessment tool be developed for, say, the symptoms of attention deficit hyperactivity disorder (ADHD)? Is asking how functional analysis may be modified or conducted even the right question? Typically developing children and adolescents may demonstrate much larger behavioral repertoires that are functionally related to a wider range of reinforcing conditions relative to children with developmental disabilities. Given this potential increased complexity of operating contingencies, functional assessment must be thorough, comprehensive, and ongoing if they are going to lead to interventions that will be effective across settings or over extended periods of time. Given the paucity of technological development in this area, clinicians often resort to assessments that rely heavily on self- and parent- reports of referral concern. Even if it is not possible to garner complete control over operating contingencies, there must be a compromise which allows us to obtain objective, reliable measurement of these variables.

Friman (2010) argued that ABA has expanded its scope, has matured as a scientific discipline, and shows great promise for continuing to extend its own generality. However, the questions posed above are the metaphorical "tip of the iceberg" relative to the potential for truly applying the methods and philosophy of behavior analysis to mainstream problems of social significance. The purpose of this article is to identify and describe some areas in need of attention (from the perspectives of those who are reporting to have a problem) and potential lines of programmatic research that may guide practice and future investigations of the influence of basic principles on assessment and treatment efficacy.

"I tried time-out. Time-out doesn't work"

If there's a phrase that should drive a behavior analyst crazy, this is it. First, let's dissect the phrase "time-out doesn't work". Time-out is, by definition, a negative punishment operation. An experimenter removes access to positive reinforcers for some pre-specified time, contingent on every occurrence of a specific response. When the time-out is matched to the function of the response, research has demonstrated that the future likelihood of the response that produced the "time-out" will decrease as a function of the contingent relationship between the response and the experimenter's delivery of the time-out. Notice that there is no reference whatsoever to the structure of the time-out, but rather a focus on the operation and the intended process. If the intended process does not occur, then the assumption about the operation was likely faulty, and the behavior analyst would start over. But in practice, most teachers and parents rarely assess the assumptions behind their use of a time-out procedure. More likely, when the child "gets on the parent's or teacher's nerves enough" (i.e., intermittent schedule), the parent or teacher requires the child to sit in a corner for a pre-specified time period (e.g. one minute per year of age) or until the child "calms down". Furthermore, the use of the time-out procedure is likely to go on regardless of its actual efficacy. In this case, notice that there is no reference whatsoever to the function of the time-out, but rather a focus on what the form of the time-out takes.

The difference between the scientifically developed and the casual use of "time-out" could not be more striking. The behavior analyst would be familiar with the basic and applied literature on punishment, which (mostly) suggests that when punishment is used as a treatment, its efficacy is directly dependent on the schedule on which it is delivered. Specifically, every response should produce the consequence (as the operation) to increase the likelihood of producing a decrease in the levels of the punished response (the process). The behavior analyst would also be familiar with research suggesting increased efficacy of punishment when it is combined with other treatment components (Foxx & Shapiro, 1978: Repp & Deitz, 1974). Mace et al. (1986) showed that time-out was equally effective whether terminating the time-out was contingent on "calming down" or response independent. In practice, is the duration of time-out a function of the child's behavior or the parent's motivation to escape from aversive interactions with their child? White, Nielsen, & Johnson (1972) found the use of time outs exceeding 15 minutes did not yield any added benefit on child behavior, but parents continue to use extended time outs. Such uses of time out likely serve negative reinforcement function (i.e., parent's behavior is maintained by escape from further aversive interactions), but how does time out duration affect parenting behavior once the child is permitted to return to the setting? What are the odds that parents and teachers are familiar with the factors that increase the efficacy of punishment, that time-out is a negative punishment operation, and the various empirical findings listed above? Unfortunately, this is just one example of the gap that needs to be bridged between what has been established in the scientific literature (i.e., evidence based) and typical practice in homes and schools.

Positive/Negative Reinforcement/Punishment and Motivating Operations. To our knowledge, there exist no published studies demonstrating that parents can be taught to define positive reinforcement, negative reinforcement, positive punishment, negative punishment, and motivating operations. Nor is there a study showing that parents can identify the antecedent conditions that occasioned and evoked responding and the consequences that maintained them. Contrast those statements with the voluminous existing literature demonstrating those relationships in analogue settings. An important area of future research that may greatly extend the efficacy of behavior analytic treatments includes teaching parents and teachers basic understanding and identification of conditions under which behavior is likely to occur, and the consequences that are likely to make those responses happen again. Specifically, if parents and teachers are provided with definitions and examples that are meaningful in their own homes and classrooms, would parents and teachers be more likely to maintain the use of the treatments, even when the effects wane intermittently? Would parents and teachers change their behavior relative to establishing motivation to engage in certain behaviors (or failing to abolish motivation) after understanding motivating operations? Would understanding the effects of intermittent reinforcement schedules, delays to reinforcement, quality of reinforcement, and response effort change how a parent responds to the occurrence of an appropriate response? What are the necessary and sufficient conditions to teach a parent or teacher to recognize when a motivating operation is present, and the proper way to respond? The treatment training literature is rich with examples of the necessary and sufficient conditions to teach a parent or teacher to execute a particular treatment with integrity. However, we are suggesting a strategy with a different focus. The focus is not on the particulars of a protocol, but rather on understanding dynamic changes in motivating operations (and the subsequent evocative efficacy of environmental events), the extent to which stimuli will function as reinforcers or punishers for particular responses, etc.

"This kid doesn't belong in regular education. Get him out of my classroom".

We assume that everyone can generally agree that it is better for children (1) to graduate from high school than to not, (2) to remain in the least restrictive environment in which students will be successful, and (3) to not be incarcerated than to be incarcerated. Superficially, this statement makes sense, and it is powerful to paint a picture of the individual student who "almost didn't make it" but "was saved". Thus the question: is it worth exhausting so many resources on one child, who is seemingly "wasting the system's time and money"? Let's look at that initial statement from a slightly different perspective. In 2005 dollars, a high school graduate will earn $26,933 per year, and a high school drop-out will earn $17,299 (U.S. Bureau of the Census, 2006). The best predictor of being in special education and remaining in special education is the initial referral for special education (Donovan & Cross, 2002). Special education status is negatively correlated with high school graduation and positively correlated with incarceration. While incarcerated, an individual costs taxpayers $25,895 per year (2008 dollars), and, at the same time, is not contributing to the tax base, because that individual is not gainfully employed (Department of Justice, 2009). These data are compelling reasons (at least financially) to consider developing technologies for ensuring that children remain in school until graduation, remain in the least restrictive environment that makes sense, and are not incarcerated.

A common question you may encounter for children who are not successful ("behaviorally" speaking) in school is, '"why won't Johnny do what he's told"? A common statement you may hear is "Johnny doesn't belong in general education. Get him out of my classroom." Unfortunately, this may be the beginning of a chain of events that, statistically speaking, seals the fate of the targeted student for special education, or worse, dropping out (U.S. Bureau of Census, 2006). What if the question above were asked differently? For example, "what environmental changes may be made to decrease the likelihood of certain responses, and increase the likelihood of other responses?" Behavior analysts have the opportunity to corner the market on effective behavior management, teach necessary skills to be successful in school, and apply contingencies to change the likelihood of undesirable behavior in schools. This might seem like a bold statement, but there are at least three reasons that make it plausible. First, there is a large cohort of students who are not successful in regular education because of behavior problems, and it is getting worse, not better (Cohn, 2001). Thus, services that are being provided are not solving some (likely large) percentage of the problems that schools are reporting. Second, ABA is rooted in basic principles that largely transcend topography and diagnosis, so there is no reason to think that the proper motivating operation manipulations and reinforcement contingencies, when effectively implemented, would not change the behavior of a children diagnosed with, say, emotional and behavior disorders. Third, the newest legislative mandates [e.g., IDEIA, 2004; No Child Left Behind ACT (NCLB), 2001; Response-to-Intervention; RTI] set the occasion for a system of continuous assessment and progressively intensive intervention that should be somewhat familiar to behavior analysts, and there should be little trouble adapting.

"Students should WANT to learn, and rewarding behavior will ruin intrinsic motivation."

If you are reading this, and you have a job, you are likely being paid. You may or may not like your job. Chances are, though, if you are not paid anymore, you are not going to continue to go to work, even if you love your job. Admittedly, this does not capture all of the variables that establish, occasion, evoke, and maintain work behavior in most individuals. Similarly, not all of the variables that establish, occasion, evoke, and maintain school behavior (academic, social, etc.) are well understood. If they were, most, if not all students would get good grades, and behavior problems would be barely noticeable.

A superficial (and clearly less than comprehensive) way to describe the environmental conditions that operate in a school might be that (1) there are general rules and associated contingencies in the school, (2) specific rules and associated contingencies in the classroom, and (3) cooperation with those rules and associated contingencies produces rewards both in school and at home, and noncooperation produces punishments both at school and at home. This might not capture the behavior of all students, but from a school's perspective, it hardly matters. Schools are likely to react to the problem child, rather than to the faulty environmental conditions that are producing and maintaining the problem behavior. Thus, what are the areas of research and practice that are likely to improve the behavior of students who are engaging in maladaptive behaviors in the schools?

Schedules of reinforcement. What are the schedules of reinforcement operating in everyday life? Numerous and concurrent are good places to start. There are countless concurrent schedules of reinforcement operating in schools, school personnel will know about some of them and not others, and will have the capacity to control some of them and not others. We know much about how behavior is allocated in concurrent schedules, technically speaking: behavior will be allocated among concurrently available alternatives in proportion to obtained reinforcement (Herrnstein, 1961; 1970). How does this apply to students in a school for decreasing response allocation towards undesirable alternatives? The first obvious answer is to reduce or eliminate the extent to which competing schedules are operating. This is often implemented in the crudest fashion: remove the offending student. However, research on the matching law is relatively absent in applied settings, with applied problems (although for notable exceptions, see: Mace, Neef, Shade, & Mauro, 1994; Neef, Mace, Shea, & Shade, 1992; Savastano & Fantino, 1994). Thus, a potentially fruitful line of research would be to evaluate how concurrent schedules may be manipulated (not necessarily eliminated) with the goal of changing response allocation away from socially maladaptive behavior and toward socially appropriate behavior.

Changing reinforcement contingencies, of course, requires understanding the basic reinforcement schedules, and how they may combine to influence responding. Fixed-ratio (FR) and variable-ratio (VR) schedules are easy to memorize, understand, recognize, and remember. Combined schedules, such as chain, tandem, conjunctive, and conjoint schedules (in which simple schedules are combined) are less likely to be understood, recognized, and remembered, even after they are memorized. The problem is that the more complex schedules are the ones that are likely operating, and treatment of maladaptive behavior that is operating under complex schedules is going to require the identification, manipulation, and implementation of complex schedules to be effective. In all likelihood, if a professional is provided with a referral for a student, the student engages in both behavioral excesses and deficits. Thus, the behavior analyst is likely to attempt a treatment that will reduce the likelihood of some behavior, and increase the likelihood of appropriate behavior. Research on conjunctive schedules may produce useful treatment strategies for parents and teachers.

In conjunctive schedules, all schedule components must be satisfied before a terminal reinforcer is delivered. For example, in a conjunctive differential reinforcement of alternative behavior (DRA) differential reinforcement of other (DRO) schedule, the individual must engage in some topography of behavior to satisfy the DRA schedule, and omit other topographies of behavior to satisfy the DRO schedule. Satisfying both components (i.e., the DRA and DRO schedules) satisfies the conjunctive DRA DRO schedule, and produces access to the terminal reinforcer. Conjunctive schedules are conducive for use in token economies (Kazdin, 1982; Kazdin & Bootzin, 1972), but the extent to which such schedules can be used to make token economies maximally effective has not been fully evaluated. For example, if powerful reinforcement contingencies are put in place to support academic and pro-social behavior, do those contingencies sustain exclusive responding towards socially appropriate behavior? Or, more likely, is it necessary to include contingencies that specifically target reduction of responses that are problematic? If those contingencies are included, are token economies more effective in the context of specific conjunctive schedules? How may one identify the optimal schedule for "cashing in" tokens such that they retain behavior-change influence?

Response class hierarchies (RCH). A response class is defined as a group of responses with the same function (Cooper, Heron, & Heward, 2007). A response class hierarchy shares the properties of the response class, and includes a predictable sequence of responding when members of the response class are exposed to extinction (e.g., Lalli et al., 1995). For example, suppose that there are 3 topographically distinct responses that are occasioned and evoked by identical stimulus conditions (e.g., seeing a friend across the street whom you have not seen in a great while) and maintained by the same reinforcer (e.g., access to your friend's attention). Those responses might include saying "hello!", waving your arms in the air, and then screaming your friend's name. As each response contacts extinction, the next response in the hierarchy is likely to occur until you obtain your friend's attention.

There have been at least 3 studies that have specifically demonstrated the existence of a response class hierarchy (Lalli et al. 1995; Mace et al., in press; Shabani, Carr, & Petursdottir, 2009). This is surprising for several reasons. First, there are relatively few experimental demonstrations of the existence of a RCH and direct demonstrations of strategies to insert new responses into a RCH. Second, few areas of research appear to be so understudied, yet hold so much promise for treatment development. Individuals who pose problems in schools are likely to include many responses in their repertoires that are maladaptive or problematic in classrooms. Are those problem behaviors maintained by the same consequences (i.e., in the same response class)? Do those responses occur in a particular order when early members of the response class are exposed to extinction? Is it better to expose problematic behavior to extinction and risk escalation of a RCH, or is it more favorable to a classroom teacher to reinforce less problematic behavior initially, but avoid escalation? What are the necessary and sufficient conditions for inserting novel responses into a RCH, to establish maintenance of that response, and to decrease the likelihood that other behaviors in the response class will be evoked when the novel behavior contacts extinction?

Conclusions

The preponderance of the data supports the efficacy of ABA for producing reliable and valid assessments and efficacious treatments. Arguably, the most developed and publicly recognized area of ABA is in developmental disabilities. While important socially, the focus on developmental disabilities potentially limits the generality of ABA as a science the scope of effective practice (Friman, 2010). We outlined some general areas in which experimenters could focus attention to improve the generality of ABA and the efficacy of programming in homes and schools. All of the areas are rooted in basic principles and their application.

The basic topics on which we focused (time-out; positive and negative reinforcement and punishment; motivating operations; schedules of reinforcement; RCH) have received differential amounts of attention in the literature. For example, time-out, positive and negative reinforcement and punishment have been well studied in both the basic and applied literature. Many schedules of reinforcement have been well studied in the basic literature. However, many of the schedules that may be operating in naturalistic environments (e.g., variable-interval, conjunctive, and alternative schedules) have been relatively less studied in the applied literature. On the other hand, RCH have been relatively understudied in both the basic and applied literature. Regardless of the attention that the topics have received to this point, we suggest that further study and application in various settings (e.g., homes, schools), populations (e.g., individuals diagnosed with ADHD or emotional and behavioral disorders), and caregivers (e.g., teachers, parents) will have multiple beneficial outcomes. First, the generality of the science may be appreciably extended if each of these topics is further studied and applied in novel areas. Second, a major goal of ABA, solving problems of social significance, would be addressed with further study. Third, each of these areas provides countless opportunities for important programmatic research. Finally, each of the topics provides the opportunity for translational research, which links basic and applied research for the mutual benefit of both fields and for advancing and improving clinical care (Lerman, 2003).

References

Baer, D. M. (1977). Perhaps it would be better not to know everything. Journal of Applied Behavior Analysis, 10, 167-172.

Borrero, C. S. W., &Vollmer, T. R. (2006). Experimental analysis and treatment of multiply controlled problem behavior: A systematic replication and extension. Journal of Applied Behavior Analysis, 39, 375-379.

Carr, E. G. (1994). Emerging themes in the functional analysis of problem behavior. Journal of Applied Behavior Analysis, 27, 393-399.

Cohn, A. M. (2001). Positive behavioral supports: Information for educators. National Association of School Psychologists: Fact Sheets; Bethesda, MD.

Cooper, J.O., Heron, T.E., & Heward, W.L. (2007). Basic concepts. In Applied Behavior Analysis--Second Edition (pp.24-45). Upper Saddle River, NJ: Pearson Education, Inc.

Department of Justice: Bureau of Prisons (2009). Annual determination of average cost of incarceration. Federal Register, 74.

Donovan, M. & Cross, C. (2002). Minority students in special and gifted education. Washington, D.C.: National Academy Press. http:www.nap.edu/catalog/10128.

Foxx, R. M., & Shapiro, S. T. (1978). The timeout ribbon: A nonexclusionary timeout procedure. Journal of Applied Behavior Analysis, 11, 125-136.

Friman, P. C. (2010). Cooper, Heron, and Heward's Applied Behavior Analysis (2nd ed. Checkered flag for students and professors, yellow flag for the field. Journal of Applied Behavior Analysis, 43, 161-174.

Hanley, G. P. Iwata, B. A. & McCord, B. E (2003). Functional analysis of problem behavior: A review. Journal of Applied Behavior Analysis, 36, 147-185.

Iwata, B. A. Dorsey, M. F. Slifer, K. J. Bauman, K. E. & Richman, G. S. (1994). Toward a functional analysis of self-injury. Journal of Applied Behavior Analysis, 27, 197-209.

Iwata, B. A. Wallace, M. D. Kahng, S. Lindberg, J. S. Roscoe, E. M. Conners, J. Hanley, G. P. Thompson, R. H. & Worsdell, A. S. (2000)Skill acquisition in the implementation of functional analysis methodology. Journal of Applied Behavior Analysis, 33, 181-194.

Johnston, J.M., & Pennypacker, H.S. (1980). Traditions of experimental design. In Strategies and tactics of human behavioral research (pp. 79-92). Hillsdale, NJ: Lawrence Erlbaum Associates.

Kazdin, A. E. (1982). The token economy: A decade later. Journal of Applied Behavior Analysis, 15, 431-445.

Kazdin, A. E. & Bootzin, R. R. (1972). The token economy: An evaluative review. Journal of Applied Behavior Analysis, 5, 343-372.

Kern, L. Childs, K. E. Dunlap, G. Clarke, S. & Falk, G. D. (1994). Using assessment-based curricular intervention to improve the classroom behavior of a student with emotional and behavioral challenges. Journal of Applied Behavior Analysis 27, 7-19.

Kern, L. Mauk, J. E. Marder, T. J. & Mace, F. C. (1995). Functional analysis and intervention for breath holding. Journal of Applied Behavior Analysis, 28, 339-340.

Lalli, J. S. Mace, F. C. Wohn, T. & Livezy, K. (1995). Identification and modification of a response-class hierarchy. Journal of Applied Behavior Analysis. 28, 551-559.

Lerman, D. C. (2003). From the laboratory to community application: Transitional research in behavior analysis. Journal of Applied Behavior Analysis, 36, 415-419.

Mace, F. C. & Lalli, J. S. (1991). Linking descriptive and experimental analysis in the treatment of bizarre speech. Journal of Applied Behavior Analysis. 24, 553-562.

Mace, F. C. Neef, N. A. Shade, D. & Mauro, B. C. (1994). Limited matching on concurrent schedule reinforcement of academic behavior. Journal of Applied Behavior Analysis, 27, 585-596.

Mace, F. C., Pratt, J. L., Prager, K. L., & Pritchard, D. (in press). An evaluation of three methods of 'saying no' to avoid an escalating response class hierarchy. Journal of Applied Behavior Analysis.

Michael, J. (1974). Statistical inference for individual organism research: Mixed blessing or curse? Journal of Applied Behavior Analysis, 7, 647-653.

Moore, J.W. & Fisher, W.W. (2007). The effects of videotape modeling on staff acquisition of functional analysis methodology. Journal of Applied Behavior Analysis, 40, 197-202.

Neef, N. A., Mace, F. C., Shea, M. C., & Shade, D. (1992). Effects of reinforcer rate and reinforcer quality on time allocation: Extensions of matching theory to educational settings. Journal of Applied Behavior Analysis, 25, 691-699.

Piazza, C. C. Fisher, W. W. Brown, K. A. Shore, B. A. Patel, M. R. Katz, R. M. Sevin, B. M. Gulotta, C. S. & Blakely-Smith, A. (2003)Functional analysis of inappropriate mealtime behaviors. Journal of Applied Behavior Analysis. 36, 187-204.

Repp, A. C., & Deitz, S. M. (1974). Reducing aggressive and self-injurious behavior of institutionalized retarded children through reinforcement of other behaviors. Journal of Applied Behavior Analysis, 7, 313-325.

Savastano, H. I., & Fantino, E. (1994). Human choice in concurrent ratio-interval schedules of reinforcement. Journal of the Experimental Analysis of Behavior, 61, 453-463.

Shabani, D. B., Carr, J. E., & Petursdottir, A. I. (2009). A laboratory model for studying response-class hierarchies. Journal of Applied Behavior Analysis, 42, 105-121.

U.S. Bureau of the Census. (2006). Income in 2005 by educational attainment of the population 18 years and over. Table 8. Washington, DC: U.S. Government Printing Office. http://www.census.gov/population/ www/socdemo/education/cps2006.html

U.S. Department of Education (2004).The individuals with disabilities education improvement act (IDEA), http://idea.ed.gw

U.S. Department of Education (2001). No Child Left Behind ACT (NCLB). http://www2.ed.gov/ nclb/landing.jhtml

White, Neilsen, & Johnson (1972). Timeout duration and the suppression of devant behavior in children. Journal of Applied Behavior Analysis, 5, 111-120.

Woods, D.W., Miltenberger, R.G., & Carr, J.E. (2006). Introduction to the Special Section on Clinical Behavior Analysis. Journal of Applied Behavior Analysis, 39, 407-411.

Author Contact Information:

Michael E. Kelley, Ph.D., BCBA

Assistant Research Professor

Dept. of Human Resource Development

407 Bailey Hall

University of Southern Maine

Gorham, ME 04038
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Author:Gadaire, Dana M.; Kelley, Michael E.; DeRosa, Nicole M.
Publication:The Behavior Analyst Today
Article Type:Report
Date:Jan 1, 2010
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